One of the simplest and the most commonly used text representation models is the Bag of Words (BoW). In this model, unique words in a text are extracted and the text is represented by a set of words; syntax, semantics, orders and positions of words are ignored [1]}. One of the standard ways in representing texts with words is to represent a text as word-based vector, the Vector Space Model (VSM), first proposed by Salton [2]}, [3]}. Each unique word in the corpus defines a dimension in the space of texts. The coefficient (weight) in each component of the vector indicates the significance of the the word in that text.
